This invention is in the field of evaluation and maintenance of pipes for carrying fluids. One aspect of this invention is more specifically directed to estimating rates of corrosion in pipelines and downhole tubing, for example as applied in the production and processing of oil, gas, and hydrocarbons.
Maintaining the integrity of piping systems is a fundamental function in maintaining the economic success, and minimizing the environmental risks, liabilities, and impact, of modern oil and gas production fields and systems. Of course, the integrity of large scale pipeline systems, such as the Trans-Alaska Pipeline System, is of substantial economic and environmental concern. In the downhole context, the integrity of metallic production casing of oil and gas wells is of concern, especially given the harsh and relatively inaccessible downhole environment. Pipe integrity is also of concern in other applications, including factory piping systems, municipal water and sewer systems, and the like. As is well known in the field of pipeline maintenance, corrosion and erosion of pipeline material by the presence and action of fluids flowing through the pipeline, will reduce the thickness of pipeline walls over time. In order to prevent pipe failure due to corrosion, it is of course important to monitor the extent to which pipe wall thickness has been reduced, so that timely repairs or replacement can be made.
The prevalent corrosion reagent in oil and gas pipelines and downhole casing is carbon dioxide (CO2). Dry CO2 gas is typically not corrosive at the temperatures in which typical oil and gas pipelines operate, but CO2 that is dissolved into water is quite corrosive. In solution, dissolution of the aqueous phase CO2 creates carbonic acid, which reacts with the steel inner surface of the pipeline, corroding the pipeline. Unfortunately, water is also typically present in oil and gas pipelines and in well casing, in one or more forms such as condensation from the gas phase, water produced from the reservoir along with the oil and gas, or water that has been injected into the reservoir to maintain reservoir pressure. The aqueous solution of CO2 into this available water thus produces the carbonic acid that is one of the main corrosive agents in modern oil and gas pipelines.
Proper monitoring and maintenance of pipe integrity depends on some understanding of the rate at which the pipeline material corrodes. The ability to predict corrosion rates of pipe material can be used in various stages of the construction and operation of a piping system to ensure pipeline integrity, at optimal cost. The prediction of corrosion rates comes into play in pipe design, for example by informing the choice of materials for the pipelines, determining pipe geometry (wall thickness, etc.), determining whether to implement a corrosion inhibition program and, if so, selecting the corrosion inhibitor, determining whether to include a corrosion monitoring system, and also designing the inspection strategy to be deployed, to name several examples. As known in the art, constant and rigorous inspection of pipe wall thickness loss is not practical, if in fact possible. In the pipeline context, corrosion rate prediction can be used in determining the frequency (temporal and spatial) of sampled pipeline inspection by way of radiography (RT) and ultrasonic testing (UT), or the temporal frequency at which “in-line inspection” (ILI) is carried out. After construction and during operation, accurate prediction of the corrosion rates can be used in risk assessment of the corrosion hazard for the piping system, for example by modeling the corrosion. Such modeling, based on predictions of corrosion rate, can also be used to determine and quantify changes in the corrosion risk over time, and as a function of location within the piping system.
A simple conventional approach to the prediction of aqueous phase CO2 corrosion rates simply relied on a “rule of thumb”. It is known that the concentration of aqueous phase CO2 corrosion depends on the equilibrium partial pressure of the gas phase CO2. A conventional rule of thumb for CO2 corrosion rate is based on this partial pressure: if the CO2 partial pressure exceeds 2 bar, “severe” corrosion is indicated; if the CO2 partial pressure is between 0.5 and 2 bar, corrosion may occur; if the CO2 partial pressure is below 0.5 bar, a non-corrosive situation is indicated.
Besides lacking precision in its determination of corrosion rate, such a “rule of thumb” model does not account for many factors that affect the actual corrosion rate. For example, it is known that the corrosion rate is more sensitive to the thermodynamic activity of CO2 in the aqueous phase than to its concentration; this activity is linked to the fugacity of the CO2 in its gas phase, which varies non-ideally with partial pressure. Environmental parameters that affect CO2 corrosion rate include water cut, characteristics of the hydrocarbon (particularly the chemical and physical mechanisms by which oil inhibits corrosion of steel), water chemistry and the source of the water in the pipe contents, iron content and solubility in the corrosive medium, the extent of corrosivity of the brine such as acetate-enhanced corrosion, the pH of the pipe contents, temperature, the presence of iron carbonate scale on the inner surface of the pipe, the presence of other reagents such as H2S, and the like. Metallurgical factors, such as the alloy composition and microstructure of the pipeline material, also significantly affect the corrosion rate. Hydrodynamic parameters of the fluid being carried by the pipeline also play a role. Such hydrodynamic parameters include the flow rate and also the flow “regime” (e.g., slug flow, stratified flow, annular flow, etc.), locations of enhanced corrosion due to water “drop out” (i.e., at locations where water local accumulates, such as at dead legs or at direction or inclination changes), and flow disturbances that change turbulence in the flow. The inherent non-uniformity of corrosion of pipe interior surfaces also complicates the prediction of corrosion rate: corrosion often appears as pitting, or mesa-type attack, or as flow-induced localized corrosion that begins at pits or mesa attack sites. The “rule of thumb” model obviously does not begin comprehend such variations in corrosion rate.
Empirical models of CO2 corrosion are well-known in the art. A popular empirical model is based on the equation or nomogram described in de Waard et al., “Prediction of Carbonic Acid Corrosion in Natural Gas Pipelines”, First International Conference on the Internal and External Protection of Pipes, Paper F1 (Cranfield, UK: BHRA Fluid Engineering, 1975). The original de Waard model used temperature and CO2 partial pressure to predict CO2 corrosion rate based on small-scale laboratory experiments. In recent years, this empirical model has been expanded to include correction factors based on various other parameters, including pH, corrosion product scale on the pipeline interior, fluid velocity, steel composition, water cut, and the like. It has been observed, however, that recent incarnations of such empirical models do not completely or accurately account for protectiveness of pipe material by corrosion product scale, especially at high temperature or high pH, as the model is intended to apply only in the absence of formation water (which can break down the corrosion film). Oil wetting is typically included in this model as an “on/off” factor, for example by assuming, for crude oil pipelines (i.e., no condensate), oil wetting and thus no corrosion for water cut below 30% and liquid velocity above 1 msec. Despite these limitations, the de Waard model, as enhanced in recent years, remains in widespread use, for example as described in Hedges et al., “The Role of Acetate in CO2 Corrosion: the Double Whammy”, CORROSION/99, Paper No. 21, (Houston, Tex.: NACE International, 1999).
By way of further background, corrosion models based on modeling specific corrosion mechanisms are known in the art. An early example of such a “mechanistic” corrosion model is described in Gray et al., “Mechanism of carbon steel corrosion in brines containing dissolved carbon dioxide at pH 4, CORROSION/1989 Paper No. 464, (Houston, Tex.: NACE International, 1989), which derived an electrochemical model of four redox reactions under varying types of kinetic control. This electrochemical model uses mixed potential theory to predict polarization curves, based on calculated Tafel constants and exchange current densities, and ultimately based on corrosion rates of the system.
Another model, described in Nesic et al., “An electrochemical model for prediction of corrosion of mild steel in aqueous carbon dioxide solutions. Corrosion, 52 (1996), pp. 280 et seq., is based on individual electrochemical reactions in a water-CO2 system, over a wide range of pH, temperature, partial pressure, and fluid velocity conditions, assuming no protective film. This is based on four cathodic reactions, and a single anodic reaction of iron dissolution. Transport processes are treated, in this model, in a simplified manner by assuming independent diffusion of each reactive species, and by using mass-transfer coefficients for the hydrodynamic systems of a rotating cylinder (for laboratory tests) and pipe flow.
Another mechanistic model is described in Nordsveen et al., “A Mechanistic Model for Carbon Dioxide Corrosion of Mild Steel in the Presence of Protective Iron Carbonate Films—Part 1: Theory and Verification”, Corrosion, Vol. 59, No. 5 (2003). In this model, electrochemical reactions at the steel surface, diffusion of species between the metal surface and the bulk including diffusion through porous surface films, migration due to establishment of potential gradients, and heterogeneous chemical reactions including precipitation of surface films, are all considered. As a result, this model has been observed to predict corrosion rate, and concentration and flux profiles for the species of interest. This approach models heterogeneous chemical reactions (e.g., precipitation of surface films), electrochemical reactions at the steel surface, and transport of species to and from the bulk (e.g., convection and diffusion through the boundary layer and the porous surface films, migration as a result of the establishment of potential gradients). The MULTICORP software package, developed by Ohio University, implements this model approach using fundamental physicochemical laws and corresponding equations; equation parameters such as equilibrium constants, reaction rate constants, and diffusion coefficients, are taken from the open literature or are based on experimental data.
It has been observed that these conventional mechanistic models are complex to implement in practice. This complexity derives from the specialized computer software that is required for numerical solution of the complex and interrelated mathematical equations.